Guher's work lies at the intersection of psychometrics, learning sciences, and artificial intelligence, driven by a fundamental question: how can learning and assessment systems be designed to work effectively for everyone? Through this work, she seeks to explain complex learner behavior and develop adaptive systems that address the needs of diverse learners. She is particularly interested in ensuring that these systems can be implemented in real-world settings, where scalability, fairness, validity, and reliability are essential to achieving meaningful impact. Having worked across both industry and academia, she has seen firsthand the importance of bridging theory with practice and ensuring that the systems developed are not only theoretically sound but also practically deployable. She is passionate about sharing these experiences and designing next-generation assessment methods that are both AI-powered and human-centered.